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If we know what our customers will want tomorrow, we can make them the perfect offer today. In an ideal world, we want to present ourselves to our customers as understanding them and their needs, and offer business models and business ideas that are actually relevant for them – rather than just ‘dumping’ another product on thousands of people at a time. Unfortunately, the reality is often quite different.

We’ve all been there: you get another letter in the post about an offer for financing at attractive interest rates. Why does everyone get these letters? Because we are customers of banks. Is the letter tailored to me? No. Is the letter trying to start a conversation about a particular topic? No. Does it feel like the bank is looking after my interests? No. The bank has a product – in this case financing – that it wants to sell. This process makes no sense for either party since the time and effort the bank puts into them rarely lead to sales (poor conversion rate). You end up simply throwing the letter away without opening it because you already know that it’s nothing more than advertising rather than something that might be of interest.

This problem can be transferred to both the business-to-business (B2B) and business-to-customer (B2C) environments. Whenever we come up with a product and only focus on the product itself, rather than the needs of the people who we want to buy it, we lack the perfect solution for them from the outset – it’s a case of, ‘if the only tool you have is a hammer, it is tempting to treat everything as if it were a nail’. That means we have to turn our perspective around and look at the needs of our clients and the clientele of financial service providers. However, this rarely happens since it’s an incredibly laborious undertaking and we often lack the structured data and models or processes to do it, so we end up doing what we were doing before.

Next-best-action – the solution to understanding customers

This is where the next-best-action approach comes in. This is an approach that enables you to anticipate the needs of your customers and identify the next steps that would be best for them. The aim is to identify the right conversation starter at the right time and to combine it with the offer of services in order to provide the best approach. This might mean, for instance, analysing rent payments in combination with savings rates, age and other parameters to determine that buying a property might be a good step for the customer. If there is further financial information available, we can even calculate the ideal location for the property in advance and prepare a tailor-made offer for customers. This would fundamentally change the interaction between banks and their customer from a blanket offer without reference to real advice based on the customers’ needs.

This approach comes with a variety of challenges, such as data protection. You always need to check what is legally possible and ethically justifiable on a case-by-case basis. We help customers do this by providing them with consultancy and developing business models for them. This is quite a complex topic, so I won’t go deeper into it in this blog post. What I will focus on, however, is how implementing the approach might look.

You can use an established procedure that processes the incoming information and identify the relevant opportunities. You can connect internal systems, external systems and websites, read them out via ready-made interfaces or custom procedures and prepare and store the information in a database. A neural network does the processing. This process identifies relevant information and assigns it to the individual entities. For example, if you are active in the B2B segment and want to understand what conversation starter you can use to approach your clientele (be they existing or new), it might be helpful to link your internal information (from your CRM, for instance) with your external information, such as press releases, news or social networks. This adds a significantly greater level of customisation, eliminates aimless, general conversation and instead starts a conversation on a specific topic, which builds trust and opens the door to long-term business. The graphic below outlines an example of how this process might look:

How does artificial intelligence help with this?

Using artificial intelligence (AI) provides huge benefits throughout the entire process, and there are two major use cases for it:

  • 1: Finding and analysing the relevant information
  • 2: Abstracting information on opportunities

This means bringing all of the customer information you currently have together and defining potential target scenarios. Historical information from the financial service provider is incredibly helpful here and enables you to be successful quickly. For example, you can see which follow-up financing is relevant, when a customer needs a new insurance policy and much more. This can be trained on historical data to create insights that lead to better customer care because it’s more focused on the individual.

A flexible, customer-centric approach

In summary, the next-best-action approach can massively support sales in both existing and new customer business. When intelligent algorithms come into play, you can discover completely new content, and customers get the service they expect from a bank. The bank, on the other hand, takes a big step towards the ‘financial home’.

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Picture Tim Strohschneider

Author Tim Strohschneider

"Complicated contexts presented simply" - Tim's motto as a problem solver in the Line of Business Banking motivates him every day, with the customer in focus, to make all topics related to intelligent automation and artificial intelligence accessible in the business context. Over 15 years in the IT world, consultant at heart, leader and innovation driver - he describes his view of current developments here in the blog and on his LinkedIn page.

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